Data Scientist

Job Title:

Location:

Experience:

Type:

Role Overview

Data Scientist

Key Tasks and Accountabilities

• Translate complex business challenges into analytical problems and deliver actionable insights.
• Own the

  end-to-end AI/ML lifecycle

  – data preprocessing, feature engineering, model training, validation, deployment, and monitoring.
• Build and deploy

  forecasting models (time-series, regression, ML-based, deep learning)

  for demand planning, promotions, and pricing optimization.
• Develop reusable and modular Python code following

  OOP best practices

  .
• Architect and optimize workflows on

  Azure Databricks

  , ensuring scalability and automation with

  Airflow/MLFlow

  .
• Apply

  MLOps practices

  , including containerization (Docker) and CI/CD pipelines (GitHub).
• Collaborate with cross-functional teams (data engineering, product, commercial, finance) to deliver production-ready forecasting solutions.
• Mentor and guide mid-level/junior data scientists, fostering a culture of technical excellence.
• Clearly communicate technical findings and business impact to both

  technical and non-technical stakeholders

  , including leadership teams.

Qualifications, Experience, Skills

Education:

• B.Tech/BE/Masters in Computer Science, Data Science, AI/ML, Statistics, or related field.

Experience:

• 8–12 years of relevant experience

  in data science and advanced analytics.
• Proven track record in

  forecasting models

  (ARIMA, Prophet, regression, ML ensembles, deep learning for time-series).
• Strong expertise in

  Python (with OOP principles)

  and

  PySpark

  .
• Hands-on experience with

  Azure cloud, Databricks, MLFlow

  , and workflow orchestration (Airflow).
• Practical experience with

  MLOps and containerization tools

  (Docker/Kubernetes).
• Strong background in deploying

  end-to-end ML solutions into production

  .

Must Have Skills:

• Forecasting & Predictive Modeling (Time-series, ML-based, Deep Learning)
• Python, PySpark, Databricks (advanced proficiency)
• Azure Cloud ML Stack (MLFlow, Airflow, CI/CD, Git)
• Strong problem-solving and communication skills

Good to Have:

• Exposure to

  Pricing and Revenue Management domains

• Product-building experience
• Experience working in global, cross-functional teams